After decades of false dawns, the age of artificial intelligence (AI) is finally upon us. AI has talked its way into consumer consciousness via Alexa and Siri, while unprecedented access to big data sources and the cloud computing power needed to analyse them enables businesses in all sectors to pursue AI projects. They are spurred by the promise of increased efficiency and revenue opportunities, but many will be disappointed. According to analyst company Gartner, 85 percent of AI projects will fail.

AI projects need time to train the algorithm and integrate it with existing systems. They are highly tailored to the individual business and cannot readily be bought off-the-shelf by businesses choosing a fast-follower strategy. This means the gap between leaders and laggards in the AI space is growing; some commentators predict that companies who fail to invest now may never catch up.

Given the pressure on businesses to adopt AI despite the high risk of failure, what are common pitfalls?

1. Pursuing AI for its own sake Pursuing AI for its own sake can lead to failure when organizations do not anticipate the complexity of integrating AI with existing technology. Such setbacks cause disillusionment that blocks future progress. If AI is on the table “because a competitor is doing it”, the

business needs to look carefully at whether it truly understands the level of investment and integration needed to make it work.

2. Underestimating the scale and variety of data required Artificial Intelligence is only as good as the data it learns from. Training an AI algorithm requires vast data sets and maintaining it involves access to continuously updated, clean and accurate data. The business may not possess the quality or volume of relevant data needed to effectively train an AI. Even if it does, poor data infrastructure, integration and weak governance all limit AI performance.

3. Immaturity of digital transformation Businesses need a certain maturity of digital transformation to benefit from deploying AI. Without digitisation of core processes companies do not have access to the global insight that tells them where AI can deliver valid benefits. This results in AI projects undertaken in a piecemeal, siloed manner—the opposite of the transformation that businesses are seeking.

4. Human cultural challenges Getting humans to trust AI insights is the critical “last mile” of integrating it into the workplace. Research indicates only 16 percent of employees trust AI-generated insight. The complexity of AI programming means AI cannot explain which factors have influenced its decision. This causes problems in trust-based scenarios when the outcome of the decision has high human impact, such as the offer of a loan, or in healthcare diagnosis.

In the light of these pitfalls, what best practices must businesses adopt to avoid them?

Make AI part of your business strategy, not all of it Businesses must take a board-level, strategic approach to identifying AI projects likely to succeed as part of the wider technology roadmap. This should be done in the context of the organization’s digital maturity to ensure the foundations are in place to support the project. If the business is simply not ready for AI, resources should be focused on making progress on the digital journey first.

The company should identify specific use cases that are important revenue generators but have lower than optimum margins, assessing whether automation could increase those margins. The same applies to processes where there is high risk of human error—automation can eliminate it to deliver a clear business benefit. John Derham of IQ Media advises: “AI should be brought in when a company is acutely aware of specific weaknesses or processes they want to scale.”

Looking outside their own organization can help businesses understand the potential and maturity of AI in their industry, recognize what it can and cannot do, and set realistic goals for introducing it into their business.

Get your data house in order Businesses should plan ahead by implementing a sophisticated data strategy that identifies external data sources to fill gaps in their proprietary data. This ensures AI projects will be sustainable and have access to training data resources.

The organization will need a sound technology infrastructure and integration capabilities to capture and manipulate large data sets from multiple internal and external sources. Internal sources must be subject to strong governance and data hygiene, while third-party datasets have to be high quality with seamless integration facilities and impeccable accuracy.

Bring human intelligence along on the journey Digital transformation does not occur in a vacuum. The cultural changes that will take place with the workforce must not be underestimated. AI has the potential to be a powerful enabler for employees, but only if understood as such.

Investment in digital upskilling and change management must occur in tandem with technology advances for businesses to fully reap the benefits of the age of Artificial Intelligence.